Performance optimization modelling of a horizontal roughing filter for the treatment of mixed greywater

Abstract

The growing demand of development of appropriate and relevant wastewater treatment technology is drastically increasing in rural and urban communities in many parts of the world including South Africa. This is largely exacerbated by the escalation of water demand and decreasing potable water availability. As a result, advanced research related to the development and optimization of water treatment technologies is becoming an urgent necessity including research focusing on wastewater recycling and reclamation. Meanwhile, horizontal roughing filter (HRF) technology is one such physical water pre-treatment system that can effectively and efficiently treat wastewater and thus reduce the reliance on potable water use. Therefore, this study aimed at modelling HRF in order to investigate the option of domestic greywater reuse for delivering desired water quality for nonpotable applications. The overall aim of the study was modelling the HRF in order to improve its performance and several objectives were investigated in this study. The first one was the characterization of biological and physico-chemical strength of greywater originated from kitchen, bath and laundry sources. The second objective investigated the HRF performance/efficiency after treating various domestic greywater pollutants. The third objective investigated the controlling factors affecting the performance and optimization of the HRF during its operation. This was investigated based on design of experiments (DOE) and response surface methodology (RSM). Based on the artificial neural network (ANN), the first objective investigated the filter duration in a HRF using ANN modelling for high level of contaminants in domestic greywater. Secondly, the ANN models applicable to a HRF were investigated and used for the prediction of greywater quality variables from the output stream of the HRF based on experimental data obtained from the operation of the HRF equipment. The first step in water treatment processes requires quality analysis in order to understand the constituent of water pollutants. Therefore, the experimental analysis of biological and physicochemical contents in greywater sources was conducted in this study. The next aspect involved treatment of mixed domestic greywater using a three compartment HRF unit which was fixed at a low filtration rate of 0.3 m/h. The effect of operating parameters on the HRF performance was studied factorial design and optimization. The factorial design application in HRF defines performance based on derivation of right factor settings for the effective operation of HRF. The aspect of ANN was undertaken to investigate the applicability, effectiveness and predictive ability of ANN within a HRF equipment. The use of ANN in HRF can serve as a monitoring tool in terms of performance and also as an indicator of any quality deviation that might be occurring during the filter operation. The key findings were obtained on qualitative analysis of domestic greywater originating from a peri-urban community for the quantification of biological and physico-chemical contaminants. The significant quality difference was recorded in greywater sources and the kitchen greywater source recorded the highest load of pollutants compared to the laundry and bathing sources at p<0.05 significant level. Furthermore, the quality difference was evident in greywater sources in terms of daily households’ social conditions, activities and practices. Also, the analysis of microbes in domestic greywater recorded high values of Escherichia coli (E. coli) and total coliform contamination which poses health related risks in domestic greywater reuse. Therefore, further treatment of domestic greywater prior to reuse remained necessary. The effectiveness of HRF was evident in removing biological and physico-chemical pollution load in domestic greywater at 0.3 m/h filtration rate. An average of 90% turbidity removal was obtained with 86% removal of conductivity and 84% of total solids and more than 50-70% removal of chemical oxygen demand (COD) within the HRF system. The E. coli and total coliforms were totally removed in the three compartment HRF. Based on DOE analysis, the significant factors identified were flowrate, gravel media, filter bed height and filter length and most significant contributing factor identified was filtration rate. Furthermore, the optimization of the HRF resulted in a high efficiency of 76% for the removal of turbidity. Results on ANN modelling for the prediction of turbidity of the effluent stream from the HRF showed good learning abilities of the ANN and the optimal ANN structure obtained was 4-7-1 structure using the trainlm algorithm. The mean square error (MSE) value below 10% was obtained after training and the R correlation coefficient >0.9 was obtained in training, testing, validation and all data sets. For the prediction of COD, the optimal ANN architecture was 3-10-1 which was obtained with trainlm training algorithm. A satisfactory mean absolute percentage error (MAPE), low mean absolute error (MAE) and high R correlation coefficients close to 1 for the training and testing sets were also recoded for this ANN model for the prediction of COD. The other objective was the investigation of filter duration in HRF using ANN and a 4-8-2 optimal structure was obtained with the trainlm algorithm which outperformed other training algorithms for the prediction of filter duration along with turbidity. Also, a high R correlation coefficient and low MSE value was obtained for this optimal ANN model for the predicted filter duration. For this model, satisfactory R correlation values for training, testing, validation and all data were close to 1. Results on feedforward multi-input multi-output (MIMO) ANN showed good accuracy in predicting multioutput parameters of domestic greywater effluent from the HRF. The optimal ANN architecture obtained through a trial-and-error approach for MIMO ANN was 7-15-4. During training, different structures of ANN were investigated through varying training functions, neurons and combination of physico-chemical parameters and learning functions. For the optimal ANN model, the MSE of 0.001 was finally obtained based on the training data set. Furthermore, the R correlation values above 0.9 for training, testing, validation and all data sets were obtained. The optimal ANN model also showed good prediction and satisfactory accuracy when a new set of sample data was presented to the network. Therefore, based on the objectives and findings of this study, the pollution load in domestic greywater characteristics can contain a number of pollutants and can significantly vary with greywater sources. It is also important to note that the HRF significantly showed effectiveness in treating physical pollutants and large amounts of chemical and biological pollutants. From the findings and based on the HRF, it was also noted that the chemical pollutants can be significantly removed using a combination of physical and chemical treatment processes in order to remove more pollutants. This was observed by a high removal of physical pollutants such as turbidity, conductivity and solids while domestic greywater biodegradability ratio was lower than 0.5. Furthermore, for the DOE/RSM techniques, it was also observed that the effective filter performance of the HRF is a function of multi-design parameters such as filtration rate, filter length, gravel media and bed height and multi factor optimization was useful in this research work. Finally, the ANN showed effective characteristics and accuracy in the HRF equipment for the prediction of multi-output variables of the effluent greywater from the HRF following mixed domestic greywater pre-treatment.